R Tutorial : Exploring raw data (part 2)

Want to learn more? Take the full course at https://learn.datacamp.com/courses/cleaning-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Okay, so we've seen some useful summaries of our data, but there's no substitute for just looking at it. The head() function shows us the first 6 rows by default. If you add one additional argument, n, you can control how many rows to display. For example, head(lunch, n = 15) will display the first 15 rows of the data. We can also view the bottom of lunch with the tail() function, which displays the last 6 rows by default, but that behavior can be altered in the same way with the n argument. Viewing the top and bottom of your data only gets you so far. Sometimes the easiest way to identify issues with the data are to plot them. Here, we use hist() to plot a histogram of the percent free and reduced lunch column, which quickly gives us a sense of the distribution of this variable. It looks like the value of this variable falls between 50 and 60 for 20 out of the 46 years contained in the lunch dataset. Finally, we can produce a scatter plot with the plot() function to look at the relationship between two variables. In this case, we clearly see that the percent of lunches that are either free or reduced price has been steadily rising over the years, going from roughly 15 to 70 percent between 1969 and 2014. To review, head() and tail() can be used to view the top and bottom of your data, respectively. Of course, you can also just print() your data to the console, which may be okay when working with small datasets like lunch, but is definitely not recommended when working with larger datasets. Lastly, hist() will show you a histogram of a single variable and plot() can be used to produce a scatter plot showing the relationship between two variables. Time to practice! #DataCamp #RTutorial #CleaningDatainR